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PurposeThe purpose of this work was to develop a knowledge-based dose prediction system using a convolution neural network (CNN) for cervical brachytherapy treatments with a tandem-and-ovoid applicator.MethodsA 3D U-NET CNN was utilized to make voxel-wise dose predictions based on organ-at-risk (OAR), high-risk clinical target volume (HRCTV), and possible source location geometry. The model comprised 395 previously treated cases: training (273), validation (61), test (61). To assess voxel prediction accuracy, we evaluated dose differences in all cohorts across the dose range of 20-130% of prescription, mean (SD) and standard deviation (σ), as well as isodose dice similarity coefficients for clinical and/or predicted dose distributions. We examined discrete Dose-Volume Histogram (DVH) metrics utilized for brachytherapy plan quality assessment (HRCTV D90%; bladder, rectum, and sigmoid D2cc) with ΔDx=Dx,actual-Dx,predicted mean, standard deviation, and Pearson correlation coefficient further quantifying model performance.ResultsRanges of voxel-wise dose difference accuracy (δD¯±σ) for 20-130% dose interval in training (test) sets ranged from [-0.5% ± 2.0% to +2.0% ± 14.0%] ([-0.1% ± 4.0% to +4.0% ± 26.0%]) in all voxels, [-1.7% ± 5.1% to -3.5% ± 12.8%] ([-2.9% ± 4.8% to -2.6% ± 18.9%]) in HRCTV, [-0.02% ± 2.40% to +3.2% ± 12.0%] ([-2.5% ± 3.6% to +0.8% ± 12.7%]) in bladder, [-0.7% ± 2.4% to +15.5% ± 11.0%] ([-0.9% ± 3.2% to +27.8% ± 11.6%]) in rectum, and [-0.7% ± 2.3% to +10.7% ± 15.0%] ([-0.4% ± 3.0% to +18.4% ± 11.4%]) in sigmoid. Isodose dice similarity coefficients ranged from [0.96,0.91] for training and [0.94,0.87] for test cohorts. Relative DVH metric prediction in the training (test) set were HRCTV ΔD¯90±σΔD = -0.19 ± 0.55Gy (-0.09 ± 0.67 Gy), bladder ΔD¯2cc±σΔD = -0.06 ± 0.54Gy (-0.17 ± 0.67 Gy), rectum ΔD¯2cc±σΔD= -0.03 ± 0.36Gy (-0.04 ± 0.46 Gy), and sigmoid ΔD¯2cc±σΔD = -0.01 ± 0.34Gy (0.00 ± 0.44 Gy).ConclusionsA 3D knowledge-based dose predictions provide voxel-level and DVH metric estimates that could be used for treatment plan quality control and data-driven plan guidance.

PurposeCurrently, there is a lack of patient-specific tools to guide brachytherapy planning and applicator choice for cervical cancer. The purpose of this study is to evaluate the accuracy of organ-at-risk (OAR) dose predictions using knowledge-based intracavitary models, and the use of these models and clinical data to determine the dosimetric differences of tandem-and-ring (T&R) and tandem-and-ovoids (T&O) applicators.Materials and methodsKnowledge-based models, which predict organ D2cc, were trained on 77/75 cases and validated on 32/38 for T&R/T&O applicators. Model performance was quantified using ΔD2cc=D2cc,actual-D2cc,predicted, with standard deviation (σ(ΔD2cc)) representing precision. Model-predicted applicator dose differences were determined by applying T&O models to T&R cases, and vice versa, and compared to clinically-achieved D2cc differences. Applicator differences were assessed using a Student's t-test (p < 0.05 significant).ResultsValidation T&O/T&R model precision was 0.65/0.55 Gy, 0.55/0.38 Gy, and 0.43/0.60 Gy for bladder, rectum and sigmoid, respectively, and similar to training. When applying T&O/T&R models to T&R/T&O cases, bladder, rectum and sigmoid D2cc values in EQD2 were on average 5.69/2.62 Gy, 7.31/6.15 Gy and 3.65/0.69 Gy lower for T&R, with similar HRCTV volume and coverage. Clinical data also showed lower T&R OAR doses, with mean EQD2 D2cc deviations of 0.61 Gy, 7.96 Gy (p < 0.01) and 5.86 Gy (p < 0.01) for bladder, rectum and sigmoid.ConclusionsAccurate knowledge-based dose prediction models were developed for two common intracavitary applicators. These models could be beneficial for standardizing and improving the quality of brachytherapy plans. Both models and clinical data suggest that significant OAR sparing can be achieved with T&R over T&O applicators, particularly for the rectum.

PurposeThe purpose of this study is to explore knowledge-based organ-at-risk dose estimation for intracavitary brachytherapy planning for cervical cancer. Using established external-beam knowledge-based dose-volume histogram (DVH) estimation methods, we sought to predict bladder, rectum, and sigmoid D2cc for tandem and ovoid treatments.Methods and materialsA total of 136 patients with loco-regionally advanced cervical cancer treated with 456 (356:100 training:validation ratio) CT-based tandem and ovoid brachytherapy fractions were analyzed. Single fraction prescription doses were 5.5-8 Gy with dose criteria for the high-risk clinical target volume, bladder, rectum, and sigmoid. DVH estimations were obtained by subdividing training set organs-at-risk into high-risk clinical target volume boundary distance subvolumes and computing cohort-averaged differential DVHs. Full DVH estimation was then performed on the training and validation sets. Model performance was quantified by ΔD2cc = D2cc(actual)-D2cc(predicted) (mean and standard deviation). ΔD2cc between training and validation sets were compared with a Student's t test (p < 0.01 significant). Categorical variables (physician, fraction-number, total fractions, and case complexity) that might explain model variance were examined using an analysis of variance test (Bonferroni-corrected p < 0.01 threshold).ResultsTraining set deviations were bladder ΔD2cc = -0.04 ± 0.61 Gy, rectum ΔD2cc = 0.02 ± 0.57 Gy, and sigmoid ΔD2cc = -0.05 ± 0.52 Gy. Model predictions on validation set did not statistically differ: bladder ΔD2cc = -0.02 ± 0.46 Gy (p = 0.80), rectum ΔD2cc = -0.007 ± 0.47 Gy (p = 0.53), and sigmoid ΔD2cc = -0.07 ± 0.47 Gy (p = 0.70). The only significant categorical variable was the attending physician for bladder and rectum ΔD2cc. CONCLUSION: A simple boundary distance-driven knowledge-based DVH estimation exhibited promising results in predicting critical brachytherapy dose metrics. Future work will examine the utility of these predictions for quality control and automated brachytherapy planning.

BackgroundTo test the hypothesis that atlas-based active bone marrow (ABM)-sparing intensity modulated radiation therapy (IMRT) yields similar dosimetric results compared to custom ABM-sparing IMRT for cervical cancer patients.MethodsWe sampled 62 cervical cancer patients with pre-treatment FDG-PET/CT in training (n=32) or test (n=30) sets. ABM was defined as the subvolume of the pelvic bone marrow (PBM) with standardized uptake value (SUV) above the mean on the average FDG-PET image (ABMAtlas) vs. the individual's PET (ABMCustom). Both were deformed to the planning CT. Overlap between the two subvolumes was measured using the Dice coefficient. Three IMRT plans designed to spare PBM, ABMAtlas, or ABMCustom were compared for 30 test patients. Dosimetric parameters were used to evaluate plan quality.ResultsABMAtlas and ABMCustom volumes were not significantly different (p=0.90), with a mean Dice coefficient of 0.75, indicating good agreement. Compared to IMRT plans designed to spare PBM and ABMCustom, ABMAtlas-sparing IMRT plans achieved excellent target coverage and normal tissue sparing, without reducing dose to ABMCustom (mean ABMCustom dose 29.4Gy vs. 27.1Gyvs. 26.9Gy, respectively; p=0.10); however, PTV coverage and bowel sparing were slightly reduced.ConclusionsAtlas-based ABM sparing IMRT is clinically feasible and may obviate the need for customized ABM-sparing as a strategy to reduce hematologic toxicity.

Objective. To lay the foundation for automated knowledge-based brachytherapy treatment planning using 3D dose estimations, we describe an optimization framework to convert brachytherapy dose distributions directly into dwell times (DTs). Approach. A dose rate kernel d ̇ (r , θ , φ) was produced by exporting 3D dose for one dwell position from the treatment planning system and normalizing by DT. By translating and rotating this kernel to each dwell position, scaling by DT and summing over all dwell positions, dose was computed (D calc). We used a Python-coded COBYLA optimizer to iteratively determine the DTs that minimize the mean squared error between D calc and reference dose D ref, computed using voxels with D ref 80%–120% of prescription. As validation of the optimization, we showed that the optimizer replicates clinical plans when D ref = clinical dose in 40 patients treated with tandem-and-ovoid (T&O) or tandem-and-ring (T&R) and 0–3 needles. Then we demonstrated automated planning in 10 T&O using D ref = dose predicted from a convolutional neural network developed in past work. Validation and automated plans were compared to clinical plans using mean absolute differences ( MAD = 1 N ∑ n = 1 N abs x n − x n ′ ) over all voxels (xn = Dose, N = #voxels) and DTs (xn = DT, N = #dwell positions), mean differences (MD) in organ D 2cc and high-risk CTV D90 over all patients (where positive indicates higher clinical dose), and mean Dice similarity coefficients (DSC) for 100% isodose contours. Main results. Validation plans agreed well with clinical plans (MADdose = 1.1%, MADDT = 4 s or 0.8% of total plan time, D 2cc MD = −0.2% to 0.2% and D90 MD = −0.6%, DSC = 0.99). For automated plans, MADdose = 6.5% and MADDT = 10.3 s (2.1%). The slightly higher clinical metrics in automated plans (D 2cc MD = −3.8% to 1.3% and D90 MD = −5.1%) were due to higher neural network dose predictions. The overall shape of the automated dose distributions were similar to clinical doses (DSC = 0.91). Significance. Automated planning with 3D dose predictions could provide significant time savings and standardize treatment planning across practitioners, regardless of experience. [ABSTRACT FROM AUTHOR]

The use of interstitial needles, combined with intracavitary applicators, enables customized dose distributions and is beneficial for complex cases, but increases procedure time. Overall, applicator selection is not standardized and depends on physician expertise and preference. The purpose of this study is to determine whether dose prediction models can guide needle supplementation decision-making for cervical cancer. Intracavitary knowledge-based models for organ-at-risk (OAR) dose estimation were trained and validated for tandem-and-ring/ovoids (T&R/T&O) implants. Models were applied to hybrid cases with 1–3 implanted needles to predict OAR dose without needles. As a reference, 70/67 hybrid T&R/T&O cases were replanned without needles, following a standardized procedure guided by dose predictions. If a replanned dose exceeded the dose objective, the case was categorized as requiring needles. Receiver operating characteristic (ROC) curves of needle classification accuracy were generated. Optimal classification thresholds were determined from the Youden Index. Needle supplementation reduced dose to OARs. However, 67%/39% of replans for T&R/T&O met all dose constraints without needles. The ROC for T&R/T&O models had an area-under-curve of 0.89/0.86, proving high classification accuracy. The optimal threshold of 99%/101% of the dose limit for T&R/T&O resulted in classification sensitivity and specificity of 78%/86% and 85%/78%. Needle supplementation reduced OAR dose for most cases but was not always required to meet standard dose objectives, particularly for T&R cases. Our knowledge-based dose prediction model accurately identified cases that could have met constraints without needle supplementation, suggesting that such models may be beneficial for applicator selection. [ABSTRACT FROM AUTHOR]

Research demonstrates that instructing patients to have a full bladder for pelvic radiotherapy results in highly variable bladder volumes at daily treatment. We aimed to determine bladder volume variation in patients with intact cervical cancer treated with intensity-modulated radiotherapy (IMRT) on an empty bladder and estimate the difference in radiation dose to the small bowel compared to treating on a full bladder. We identified 29 patients treated with IMRT from 2010 to 2013 who underwent 2 planning computed tomography (CT) scans, 1 with a full bladder followed by 1 with an empty bladder. Interfractional variation in bladder volume was measured using 782 daily cone beam computed tomography (CBCT) scans. To estimate dose to small bowel, radiation plans were created on both empty and full bladder CT scans using an automated knowledge-based planning modeling program. Mean bladder volume with empty bladder instructions was 67 ± 26 cc compared to 91 ± 43 cc for no bladder instructions and 154 ± 54 cc for full bladder instructions (p < 0.001). There was a significant reduction in the absolute bladder volume variation in patients given empty bladder instructions compared to full bladder instructions (p < 0.05) The intraclass correlation coefficient showed low reliability of bladder filling across all groups (p = 0.6). The average bowel V45 for the empty bladder plans was 188 cc, compared to 139 cc for the full bladder plans (p < 0.05). More plans created on an empty bladder exceeded Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC) guidelines but this was not significant (31% vs 14%, p = 0.12). Reliability of bladder volume at the time of radiation treatment is low, regardless of bladder filling instructions, although an empty bladder reduces absolute variation in bladder volume. Radiation planning on an empty bladder predicts a larger volume of small bowel receiving 45 Gy compared to a full bladder, although bowel dose on average is still within QUANTEC guidelines (V45 < 195 cc). [ABSTRACT FROM AUTHOR]